There are object detection methods utilized for detecting a target object from inside an image. As technologies related to the object detection methods, there are a background difference method and an interframe difference method.
The background difference method detects a target object by taking out the difference of an input image from its background image (an image which does not include the target object). According to the background difference method, it is possible to successfully detect a target object which moves at times and stops at other times. On the other hand, for the background difference method, it is necessary to prepare a background image which does not include the target object in advance. Further, a problem occurs that it is difficult to acquire a correct detection result when the background changes due to illuminative variation and the like.
On the other hand, the interframe difference method detects a target object by taking out the difference between input images different in time (interframe images). Different from the background difference method described hereinabove, the interframe difference method does not need to prepare a background image which does not include the target object in advance and, at the same time, is tolerant of background change due to a slow-paced variation in illumination such as environment light. However, with the interframe difference method, a problem occurs that only part of a target object (an outline and the like) can be detected with some image pattern of the target object. Further, with a motionless target object, another problem occurs that because the interframe image almost does not change, it is difficult to detect the target object.
For mending the problem with the background difference method described hereinabove, there are methods which sequentially update the background image. An example is described in the accompanying Patent Document 1. The method described in the Patent Document 1 sequentially updates the background image by a simple add-in method, and detects the target object by the background difference method with the updated background image. By virtue of this, because the sequentially updated background image is utilized, it is unnecessary to prepare a background image which does not include the target object in advance, and also possible to cope with background change due to a slow-paced variation in illumination such as environment light.
However, with the method disclosed in the Patent Document 1 described hereinabove, a problem occurs that when the target object moves slowly at times and stops at other times, the target object will be updated by the background image, thereby making it difficult to detect the target object. Further, the background update cannot catch up with a background which changes continuously such as with a wobbling tree, a fluctuant water surface, and the like (environmental changing). Thereby, it is difficult to appropriately detect the target object.
Therefore, for solving the problem described hereinabove, there are methods which estimate the background by approximating it to a mixture distribution model. An example is described in the accompanying Nonpatent Document 1. The method described in the Nonpatent Document 1 carries out the process by the pixel, estimates the background by the mixture distribution model from the time series of the pixel value regarded as the background, and seeks for a good fitness of an input pixel value to the background so as to detect the target object. In this manner, because the background is estimated only from the pixel value regarded as the background, even if the target object moves slowly at times and stops at other times, the target object will not be updated by the background. Thereby, a stable object detection is possible under those conditions. Further, because it is also possible to express a background which changes continuously such as with a wobbling tree, a fluctuant water surface, and the like as a mixture distribution model, a favorable object detection is also possible under those conditions.
Other technologies are also known in relation to the object detection methods. For example, the accompanying Patent Document 2 discloses a pattern recognition method utilizing a posterior probability. Further, the accompanying Patent Document 3 discloses an object detection method utilizing a probability value table of an occurrence probability.    [Patent Document 1] JP 07-31732 B    [Patent Document 2] JP 2006-155594 A    [Patent Document 3] JP 2006-185206 A    [Nonpatent Document 1] Stauffer and Grimson: “Adaptive background mixture models for real-time tracking”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, pp. 246-252, 1999.
However, with the technologies disclosed in the patent documents and nonpatent document described hereinabove, there are problems as described hereinbelow. The first problem is that there is a case that a part of the target object includes a pixel value similar to the background, thereby lowering the detection performance. The reason is that especially with the technology disclosed in the Nonpatent Document 1, even viewing from the background expressed by a mixture distribution model, it is still impossible to distinguish the portion of the object with a pixel value similar to the background from the ordinary background. Further, because of this, the pixel value of the target object mistakenly recognized as the background will be updated as the background, thereby lowering the estimation precision of the mixture distribution model for the background, and thereby affecting the object detection.
The second problem is that when the trend of the background changes in part for an instant (for example, when the wind changes in intensity instantaneously so that part of the tree changes in the manner of wobbling for an instant), during that instant, a small area of the background may be mistakenly detected as the object. The reason is that especially with the technology disclosed in the Nonpatent Document 1, even viewing from the background expressed by a mixture distribution model, it is still impossible to distinguish the small area of the background mistakenly detected from the ordinary foreground (target object).
Further, utilizing the technologies of the Patent Documents 2 and 3 described hereinbefore cannot solve those two problems either. That is, with the method utilizing a posterior probability or an occurrence probability only, the precision of object detection may still be lowered if a part of the target object includes a pixel value similar to the background, or if the trend of the background changes in part for an instant.